T h e Hyper-Geometric Distribution software reliabilit y growth Model ( H G D M ) has been used f o r estimating the number of initial faults in a software program. Another important problem in the software development process is t o determine when t o stop testing and release the software. I n this paper, we investigate the optimal release policies minimizing the total expected software cost with a scheduled software delivery t i m e for the H G D M . T h e total expected software cost includes the penalty cost which should be paid by the manufacturer if the software is delivered after the scheduled delivery time. T h e m a i n result is that the optimal release t i m e can be determined and shown t o be finite. Numerical examples illustrating the optimal software release problem are also presented.
Debugging actions durin.g the test/debug phase of software d e ve1op.m e nt are la ot always performed perfectly. That is, not all th.e software faults detected are perfectly removed without introducing new faults. This phenomenon is called the imperfect debugging. The Hyper-Geo ni et ric Dist 1-i but i on software relia bilat y growth Model (HGDM) was developed for estimating the number of software fatilts initially in a program. In this paper, we propose a'n extended model based on the HGDM incorporating the notion of imperfect debugging.
In this paper, we show how several existing software r eliability growth models based on nonhomogeneous Poisson processes NHPPs can be c omprehensively derived by applying the concept of the weighted arithmetic, weighted g e ometric, or weighted harmonic mean. Furthermore, based on these three weighted means, we thus proposed a more general NHPP model from the viewpoint of quasi arithmetic mean. Under this general framework, we can not only derive some existing NHPP models but also can derive some new NHPP models.
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